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On the Lift, Related Privacy Measures, and Applications to Privacy–Utility Trade-Offs †
This paper investigates lift, the likelihood ratio between the posterior and prior belief about sensitive features in a dataset. Maximum and minimum lifts over sensitive features quantify the adversary’s knowledge gain and should be bounded to protect privacy. We demonstrate that max- and min-lifts...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137968/ https://www.ncbi.nlm.nih.gov/pubmed/37190467 http://dx.doi.org/10.3390/e25040679 |